Monday, November 17, 2014

Machine Learning (ML) is one of the most popular field in Computer Science discipline, but is also the most feared by developers. The fear is primarily because it is considered as a scientific field that requires deep mathematical expertise which most of us have forgotten. In today's world, ML has two disciplines: ML, and Applied ML. My goal is to make Machine Learning easier to understand for developers through simple applications. In other words, bridge the gap between a developer and a data scientist. In this blog, I will provide you with a step-by-step guide for building a Linear Regression model in AzureML to predict the price of a car. You will also learn the basics of AzureML along the way, as well as its application it in real-world by creating a Windows Universal Client app.

What is AzureML?

AzureML is meant to democratize Machine Learning and build a new ecosystem and marketplace for monetizing algorithms. You can find more information about AzureML here.

Why AzureML?

Because it is one of the simplest tools to use for Machine Learning. AzureML reduces the barriers to entry for anyone who wants to try out Machine Learning. You don’t have to be a data scientist to build Machine Learning models anymore.

Logical Machine Learning Flow

Figure below illustrates a typical machine learning process with end result in mind.

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Conclusion

AzureML is a new and highly productive tool for Machine Learning. It may be the only tool that lets you publish a machine learning web service directly from your design environment. Machine Learning is a vast topic and Linear Regression models discussed in this article only scratches the surface of the topic. In this article, I went over a stale dataset to showcase AzureML as a predictive analytics tool. You can apply the same procedures and components for Classification and Clustering models. Finally, my goal was in writing about Applied Machine Learning. I am not a Data Scientist, but now with all the productive tools, I feel that I can put to work some of the great algorithms that scientists have already invented.